Stream-temporal Querying with Ontologies. Möller, R.; Neuenstadt, C.; and Özçep, Ö. L. In Nicklas, D. and Özçep, Ö. L., editors, HiDeSt '15---Proceedings of the First Workshop on High-Level Declarative Stream Processing (co-located with KI 2015), volume 1447, of CEUR Workshop Proceedings, pages 42--55, 2015. CEUR-WS.org.
abstract   bibtex   
Recent years have seen theoretical and practical efforts on temporalizing and streamifying ontology-based data access (OBDA). This paper contributes to the practical efforts with a description/evaluation of a prototype implementation for the stream-temporal query language framework STARQL. STARQL serves the needs for industrially motivated scenarios, providing the same interface for querying historical data (reactive diagnostics) and for querying streamed data (continuous monitoring, predictive analytics). We show how to transform STARQL queries w.r.t.\ mappings into standard SQL queries, the difference between historical and continuous querying relying only in the use of a static window table vs.\ an incrementally updated window table. Experiments with a STARQL prototype engine using the PostgreSQL DBMS show the implementability and feasibility of our approach.
@inproceedings{moeller15stream-temporal,
  abstract = {Recent years have seen theoretical and practical efforts on temporalizing and streamifying ontology-based data access (OBDA). This paper contributes to the practical efforts with a  description/evaluation of a prototype implementation for the stream-temporal query language framework STARQL.  
 STARQL serves the needs for industrially motivated scenarios, providing the same interface for querying historical data (reactive diagnostics) and  for querying streamed data (continuous monitoring, predictive analytics).  We show how to transform STARQL queries w.r.t.\  mappings into standard SQL queries, the difference between historical and continuous querying relying only in the use of a static window table vs.\ an incrementally updated window table. Experiments with a STARQL prototype engine using the PostgreSQL DBMS show the implementability and feasibility of our approach. },
  added-at = {2015-09-29T11:04:26.000+0200},
  audience = {academic},
  author = {M\"oller, Ralf and Neuenstadt, Christian and {\"Oz\c{c}ep}, {\"Ozg\"ur} L.},
  biburl = {http://www.bibsonomy.org/bibtex/2687856184a36efc71dc70cc97d822718/oezcep},
  booktitle = {{HiDeSt '15}---Proceedings of the First Workshop on High-Level Declarative Stream Processing (co-located with {KI 2015})},
  editor = {Nicklas, Daniela and {\"Oz\c{c}ep}, {\"Ozg\"ur} L.},
  interhash = {bdf5b5774bd3f6a31534e7afba3e7c42},
  intrahash = {687856184a36efc71dc70cc97d822718},
  keywords = {optique-project obda},
  pages = {42--55},
  partneroptique = {UzL},
  publisher = {CEUR-WS.org},
  series = {CEUR Workshop Proceedings},
  timestamp = {2015-09-29T13:42:47.000+0200},
  title = {Stream-temporal Querying with Ontologies},
  volume = 1447,
  wpoptique = {WP5},
  year = 2015,
  yearoptique = {Y3}
}
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